Complex machinery is critically important to the functionality of many modern industrial systems. Rotating machines, such as turbines, generators, motors and engines are particularly important due to their ability to facilitate the process of converting energy to work. To achieve this rotating machinery typically comprises, or are at least configured with, gear systems. These machines operate via the rotation of interconnected shafts with attached gears in order to drive a load. In applications involving large loads, such as ore crushes and bar mills, gears, shafts and other components within the machines are placed under extreme stress. Ensuring the efficient and safe operation of machines in these environments is essential in order to maintain safety and maximise productivity.
Preventing damage to rotating machinery involves the accurate identification and isolation of faults within the parts of a given machine. Gear boxes are particularly vulnerable to faults due to the high stresses exerted onto the individual tooth and bearing parts. In large and complex machines fault detection is difficult due to the size of the gears and a lack of physical access afforded to operators when the machine is in use. As there are often significant costs incurred when a machine is shutdown and disassembled, there is a need for methods which can accurately perform fault detection while the machine is operating, and without requiring physical access to the gear, bearing or shaft components.
A common approach to machine fault diagnosis involves the use of sensors to take measurements from a reference component, such as a rotating shaft, to which there is access. An analysis of the sensor measurements may be performed and an alarm may be raised to indicate a fault if the values from the set of metrics used exceed a ‘safe’ threshold limit. Measurement and analysis of vibration information offers a cost effective and non-destructive mechanism for performing fault detection on operating machinery. Vibration of a rotating shaft may be measured by a sensor, with the data measurements subsequently processed via frequency based methods such as spectral analysis, wavelet analysis, or Short-Time Fourier Transforms (STFTs). The results of the processing may be used to monitor the state of the individual gear or bearing components, even when these components cannot be directly accessed or observed.
Traditional vibration measurement and analysis techniques are characterised by the use of spectral based time-frequency analysis. These methods perform analysis based on the content of the spectrum, and particularly the harmonics of a shaft rotating at a given speed, to identify gear or bearing damage. However, previous methods of vibration analysis have generally failed to accurately and reliably detect faults in complex machines. The drawbacks of these past approaches include:                1. a reliance on a constant shaft speed, and        2. a tendency to search for gear and bearing artefacts within the same spectral components.        
In practice the rotational speed of a machine may not be constant, due to the presence of start-up and shutdown phases, load variation or motor wandering during operation, and as a result the spectrum bandwidth varies leading to an inaccurate fault detection capability. If the signals measured include vibration from a number of separate parts operating under different conditions, then the presence of faults in one part may be masked by the normal operation of the others.
It is therefore desired to alleviate one or more difficulties of the prior art, or to at least provide a useful alternative.